Category: Papers, distilled

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Identifying a novel mechanism that boosts the clearance of dead cells by macrophages

Cell death is an important process through which the structure of our bodies are shaped throughout development. For example, soft tissue cells between the fingers and toes undergo apoptosis (programmed cell death) to separate the digits from each other during development (Figure 1). Billions of cells die in our bodies every day and a prompt clearance of dead cells and their debris is important for maintaining tissue homeostasis. Tissue homeostasis requires a very tight control of the balance between cellular proliferation and differentiation.  The majority of these dead cells are cleared by macrophages, a type of immune cell,  through a process called “efferocytosis”. 

Figure 1: Programmed cell death is an important process during development that serves to remove superfluous cells and tissues. Figure was adopted from “Mechanical Regulation of Apoptosis in the Cardiovascular System”.

If dead cells are not appropriately cleared by macrophages, they start leaking material in the cellular environment that causes inflammation and tissue damage. Efficient efferocytosis prevents this from happening, and thereby protects tissues from inflammation. Macrophage-mediated efferocytosis is an important process to promote the resolution of inflammation and restore tissue homeostasis. While inflammation causes swelling, redness, and pain, efferocytosis does not. In fact, enhancing efferocytosis has the potential to dampen inflammation and reduce tissue necrosis which is caused by injury or failure of the blood supply. Defective efferocytosis contributes to a variety of chronic inflammatory diseases such as atherosclerotic cardiovascular disease, chronic lung diseases, and neurodegenerative diseases. Understanding the mechanisms that regulate efferocytosis could help us develop novel therapeutic strategies for diseases driven by defective efferocytosis and impaired inflammation resolution.

Like other cells in the body, macrophages need energy to maintain their activity. Glycolysis and oxidative phosphorylation are two major metabolic pathways to provide energy for cells. Glycolysis is a process in which glucose (sugar) is broken down through enzymatic reactions to produce energy. Macrophages take up glucose via glucose transporters on the cell surface, such as GLUT1. Glucose will be broken down to generate ATP (energy) and lactate, an end product of the glycolysis pathway.

A research group in the department of Medicine at Columbia University led by Dr. Maaike Schilperoort, a postdoctoral research scientist in Dr. Ira Tabas’ laboratory, identified a novel pathway in which efferocytosis promotes a transient increase in macrophage glycolysis via rapid activation of the enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2 (PFKFB2), a key enzyme in the glycolysis pathway to convert glucose to lactate (Figure 2). 

Figure 2: The engulfment of apoptotic cells by macrophages through efferocytosis increases glucose uptake via the membrane transporter GLUT1. Glucose is broken down into lactate through glycolysis, and this process is boosted by efferocytosis through activation of the enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2 (PFKFB2). Lactate subsequently increases cell surface expression of the efferocytosis receptors MerTK and LRP1. These efferocytosis receptors facilitate the  subsequent uptake and degradation of other apoptotic cells in the tissue. This figure was created using Biorender.

MerTK and LRP1 are so-called “efferocytosis receptors” that allow the macrophages to  bind to dead cells before they can engulf and degrade them. The current study found that the production of lactate leads to an increase in MerTK and LRP1 on the cell surface in a calcium-dependent manner to drive continual removal of dead cells (Figure 2). The authors mentioned that lactate promotes an efferocytosis-induced calcium-raising mechanism that could be involved in the mitochondria division. The mechanism of how lactate promotes the increasing of calcium is not well understood and needs to be explored more in the future. This finding provides potentially new therapeutic strategies for improving cell death clearance such as targeting an endogenous inhibitor of PFKFP2. This novel finding was published in Nature Metabolism in February 2023. 

Reviewed by: Maaike Schilperoort , Erin Cullen, and Sam Rossano

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Using AI to identify high-risk patients in home health care

The population is aging. Over the past two decades, life expectancy in the United States has increased by more than five years to approximately 80 years in 2020 and is projected to further increase to over 85 years in 2060. The progressive rise in life expectancy leads to a growing share of older people in society, which increases the demand for health care services including home health care (HHC). HHC is defined by medical services that are provided in a patient’s home, usually by skilled nurses. An important aspect of HHC is the identification of patients who are at high risk for emergency care. A substantial fraction of HHC patients has to visit the emergency room or is admitted to the hospital during the HHC period. Strikingly, up to 30% of these events could be avoided by accurate risk prediction and preventative care. For example, patients identified as high-risk can be monitored more closely and treated with additional medications to prevent adverse health outcomes.

The research of Columbia postdoc Jiyoun Song is aimed at improving risk prediction in the setting of HHC. In recent work published in the Journal of Advanced Nursing, Dr. Song and colleagues performed cluster analysis, an unsupervised machine learning method to aggregate available patient data into groups. Such a clustering method is useful in identifying patterns of risk factors that interact with each other, rather than examining individual risk factors. The analysis was performed on structured data from electronic health records, that include patient characteristics such as socio-demographics and health conditions, as well as unstructured data from clinical notes written by nurses. The approach of Dr. Song and colleagues to use data derived from these clinical notes is quite innovative. They extracted risk factors from these notes through the artificial intelligence (AI)-based tool of natural language processing. Natural language processing can be used to extract meaning from text written by a human, which forms the foundation of AI chatbot systems such as the recently developed ChatGTP. The use of clinical notes as an additional source of data significantly improves risk prediction in the HHC setting, as previously shown by Dr. Song.

Through AI-assisted cluster analysis of both the structured and unstructured data, three clusters of risk factors were identified with distinct characteristics: (1) impaired physical comfort with pain, (2) high comorbidity burden (i.e., a person is suffering from more than one physical disease or condition at the same time), and (3) impaired cognitive/psychological and skin integrity. Classifying patients in these three categories could help tailor individualized preventive strategies. For example, a pain management strategy may work best for patients in cluster 1, while patients in cluster 3 may benefit mostly from psychological counseling and wound management. The study also found that these clusters are associated with the frequency and timing of emergency room visits. Patients that fall into cluster 3 have the highest need for emergency care, with 15.7% of patients being hospitalized or visiting the emergency department within the first 60 days of HHC (see Figure below).

AI-assisted cluster analysis of both structured data (i.e., electronic health records) and unstructured data (i.e., clinical notes) identified three clusters of risk factors with distinct characteristics. The risk of hospitalizations or emergency department (ED) visits is different for each cluster. Such a cluster-based analysis is useful for identifying high-risk patients in home health care and implementing preventative strategies. © 2022, Maaike Schilperoort

The results from this study suggest that implementation of cluster-based risk prediction models into early warning systems could reduce the likelihood of HHC patients being admitted to the hospital. This illustrates the potential of AI-based methods for clinical risk prediction. The use of AI is getting increasingly popular and is now also implemented in various other aspects of healthcare, such as in-hospital decision making, predicting treatment benefit, and personalizing medicine. To what extent will AI be incorporated in our healthcare system? Only time will tell.

Reviewed by: Jiyoun Song, Pei-Yin Shih, Trang Nguyen, and Sam Rossano

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Seeing more of the Unseen – using vibrational contrast and MARS to improve microscopy

Biomedical imaging is an important tool in science because it allows scientists to see what may not be visible to the human eye. Using light within the visible spectrum, microscopy allows us to see cells and their functional subunits called organelles, which can be thought of as the internal organs of a cell. We can also visualize certain proteins that may be expressed within certain organelles using fluorescence microscopy. With fluorescence microscopy, proteins in tissue or cells are tagged with light emitting markers, called fluorophores. Fluorophores make proteins under the microscope light up like fireflies on a dark summer night. Different color fluorophores can be used simultaneously to image different proteins at once, however this is limited by the number of colors available in the visible light spectrum. This means that with fluorescence imaging on a confocal microscope, there are a limited number of proteins that can be imaged within a given sample. 

That’s where MARS comes in (not the planet!)! Manhattan Raman Scattering (MARS) is a special dye pallet that, combined with signals from an electronic pre-resonance Stimulated Raman Scattering microscopy (epr-SRS), creates a very sensitive way to probe, visualize and image organelles with vibrational contrast, as opposed to just light contrast. Vibrational contrast detects molecules based on their chemical properties. For example, if a probe molecule has a double bond, it will have a different vibrational frequency than a molecule with a single bond. With this technology, you can differentiate cellular targets by using dyes/probes that vary by light and vibrational signals, making these techniques very sensitive. However, these MARS dyes are difficult to chemically synthesize, and there were initially only a limited number of usable MARS dyes.

Columbia postdoc Dr. Yupeng Miao and colleagues published an article in 2021, summarizing their development of new MARS dyes that have different properties that are easier to synthesize and can visualize even more of the cell’s proteins under the microscope at once! The research contributed 30 new MARS probes that can specifically label various proteins of interest within a given sample.

Before synthesizing these new MARS probes, the researchers designed and simulated models for each potential dye. For the design, they used a similar foundation to the previous MARS probes, but included some adjustments like changing the core atom or substituting stable isotopes throughout the molecule. The results from the design models gave the researchers confidence that they could synthesize these edited molecules to expand the list of available MARS probes.

Indeed, they expanded the list of probes by developing 30 new molecules that are able to label specific cell organelles and functions. For example, MARS probes were used to image subcellular structures including the protein alpha-tubulin, which is a protein within microtubules that provide structural support to the cell, as well as fibrillarin, which is a protein that is used as a nucleoli marker. MARS probes were also shown to successfully target the cell membrane, mitochondria, lysosomes, and other lipid structures within the cell. Even more exciting – this technology allows researchers to probe each of these cellular structures simultaneously, moreso than can be done with current fluorescent microscopy methods. This means that the new MARS probes can be used to image multiple cellular markers within the same sample!

With this technology, scientists can now see even more of the unseen, which can expand our knowledge on cellular (dys)function in health and disease.

Edited by: Maaike Schilperoort, Trang Nguyen

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The Importance of Consistent Sleep for Memory Retrieval at the Neural Level

Sleep helps us remember the details of past events more clearly. When we sleep, neural mechanisms facilitate the consolidation of memories formed during the waking day. Specifically, memories are temporarily stored in a brain structure called the hippocampus. During the consolidation process, memories are replayed and integrated into long-term storage centers in the neocortex of the brain. Poor sleep impairs sleep-based memory consolidation and memory retrieval. In other words, when our sleep is fragmented, our memory is less clear. 

One way to assess the clarity of a memory is to measure neural similarity, or the overlap between patterns of neural activity.  My colleagues and I presented participants with a series of word pairs to remember while we recorded their neural activity using electroencephalography. We used this task to measure neural activity when participants studied (i.e., encoded) and were tested on (i.e., retrieved) the word pairs. The overlap between their neural patterns for a given word pair at study and test is an index of neural similarity.

Interestingly, we found that sleep quality was associated with neural activity for word pairs that were paired differently. When people had more consistent sleep quality from night-to-night (measured with wrist-worn monitors), they had greater neural similarity when they correctly rejected word pairs that were paired differently. For example, if they saw the pair “wing – clock” during the study period and correctly identified “fork – clock” as a different pairing at test, they demonstrated higher neural similarity. 

There were several strengths of the study. We used an objective measure of sleep quality — wrist-worn monitors. We also measured sleep quality for seven nights, which allows for assessing night-to-night sleep variations. Our participants were racially and ethnically diverse people across the adult lifespan. However, our study was limited by its small, convenience-based sample of participants (74 people) and cross-sectional design. We cannot determine if poorer sleep causes lower neural similarity with this data. 

Taken together, our study suggests that memory integrity, or the ability to clearly remember the details of past events, may be linked with consistent sleep patterns. Thus, in addition to sleeping for enough time, sleep consistency also contributes to better memory retrieval. 

Edited by: Trang Nguyen, Pei-Yin Shih

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Engineered bacteria enhanced the current therapeutics in lung cancer

Lung cancer is the second most common type of cancer and is responsible for the most cancer-related death in the U.S. The American Cancer Society reports that more than 235,000 people were diagnosed with lung cancer in 2021. There are three major types of lung cancer: non-small cell lung cancer (85% of cases), small cell lung cancer (10% of cases), and lung carcinoid tumor (5% of cases). The causes of lung cancer include but are not limited to smoking, secondhand smoke, exposure to certain toxins, and family history. The symptoms include cough with blood, chest pain, wheezing, and weight loss when the cancer is in the advanced stage. Depending on the type of lung cancer and what stage it has progressed to, the treatment will be different. Broadly the treatment involves surgical resection, radiation, chemotherapy, targeted therapy and immunotherapy. However, to treat this complex disease researchers are always looking for new and improved treatment modalities.

A research group at Columbia Engineering led by Dr. Dhruba Deb in the lab of professor Tal Danino developed a new therapeutic to treat non-small cell lung cancer (NSCLC) by combining an engineered bacteria with targeted therapy to enhance the treatment efficacy without any additional toxicity in laboratory animal models. This finding was published in Scientific Report on December 13, 2022.

By engineering a toxin named theta (θ) toxin in the bacteria S.typhimurium and by testing the response of a variety of NSCLC cells to this engineered bacteria, the research group found that θ toxin can kill a variety of NSCLC cells even with different genetic background such as mutated growth factor receptor like KRAS or EGFR, the most common mutations found in NSCLC. The research group also administered locally live S.typhimurium expressing theta toxin (Stθ) in NSCLC tumor cells in the mouse model and found a 2.5-fold reduction of tumor growth within a week compared to the control group. 

With the success of testing live S.typhimurium expressing theta toxin (Stθ) in mouse model and no toxicity  found in the peripheral organs, the research group tested whether using the engineered bacteria could enhance the efficacy of the standard of care chemotherapies as well as small molecular inhibitors. To identify potential drugs to combine with Stθ, the authors used RNA-sequencing. This helped to pinpoint which biochemical pathways in NSCLC cells were helping the cells to survive the Stθ treatment. To overcome this ability of NSCLC cells, the researchers blocked those biochemical pathways with drugs and eliminated the NSCLC cells. For example, one of the drugs, MK2206 when paired with Stθ treatment, blocks the NSCLC cells’ ability to survive via biochemical signaling of phosphorylated AKT that promotes survival and growth in response to extracellular signals. Key proteins involved are PI3K (phosphatidylinositol 3-kinase) and Akt (protein kinase B)..  The research group also tested the combination treatment of MK2206 and Stθ in a mouse model. They found that the combination treatment of Stθ bacteria and MK2206 suppressed the tumor growth efficiently compared to treatment with only Stθ or only MK2206. Moreover, with lower dose of bacteria and drug use, they could observe similar treatment results and could possibly avoid  the activation of the immune system caused by high dose of bacteria treatment. Taken together, this combination treatment is a potential therapeutic for the NSCLC. 

There are several limitations in this study that need to be addressed before entering the clinical trial. First, the authors used a small number of animals per cohort in the in vivo study, so they plan to expand their study to assess the overall survival upon treatment. Second, the toxins themselves are not selectively targeted to the cancer cells, so they need to develop a selective delivery method to avoid the systemic toxicity. In the laboratory animal models, the local administration of the live bacteria acted as a selective delivery. But, further studies are necessary to use this live bacteria in human clinical trials. Overall, this study opens up new treatment options for patients diagnosed with the NSCLC.

Reviewed by: Sam Rossano, Margarita T. Angelova

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A continuous war between DNA elements shapes genome evolution

While genomes include the totality of genes that determine an organism’s biological identity, genes can only be a tiny fraction of the genome. In humans, and in many other species, DNA contains multiple diverse regions. One example are transposons, or mobile genetic elements, which are parts of DNA that can move in new places in the genome (Figure 1). The “genomic walks” of transposons are potentially harmful, since when they “jump” within a gene, they trigger mutation and loss of that gene. Cells are constantly evolving new strategies to keep transposons under control. In response, transposons adapt and arm themselves with new strategies to escape cellular control.

Sometimes, a mobilized transposon hijacks additional DNA information that it transfers to the new location with itself. When this additional DNA happens to be a gene providing survival advantage, not only the recipient increases its gene pool and survival capacities but the transposon also increases its chances to propagate. This process of transmission of information between different species is termed horizontal gene transfer and represents a major driver of genome evolution across all domains of life. Transposons have been key players in this process.

Figure 1. Schematic representation of a DNA transposon and its movement between two DNA molecules. Image created with BioRender.com.

Transposons can be considered as reminiscent of ancient viral infections that managed to integrate in a host cell genome but later lost the capacity to completely exit the cell. Bacteria have evolved a fascinating mechanism to remember viruses that they have already encountered. This acquired immunity involves a family of DNA sequences named CRISPR and their associated protein partners Cas. The CRISPR sequences are fragments of DNA derived from different viruses that have infected the bacteria and were integrated in the bacterial genome, creating a footprint of previous infections (Figure 2, upper part). When cells are infected, they use the CRISPR catalog and compare it to the invading DNA, helping it to recognize recurrent viruses and destroy them more rapidly. Cas proteins participate in the degradation of the viral DNA. (Figure 2, lower part).

Advances in research showed that the CRISPR-Cas complexes can be modified to edit genes in different organisms. To do this, part of the complex is changed to lead the Cas protein to a gene of interest, instead towards a viral genome. This system of gene editing has already found numerous important applications ranging from basic biology research to disease treatments and development of new technologies. The discovery of CRISPR-Cas was acknowledged with the 2020 Nobel Prize in Chemistry to Emmanuelle Charpentier and Jennifer Doudna.

Figure 2. Schematic representation of the CRISPR-Cas9 adaptive immune system of bacteria. Briefly, upon infection the viral DNA is fragmented and part of it is integrated in a special region of the bacterial genome, the CRISPR locus. CRISPR sequences get copied into shorter RNA molecules that carry parts of sequences identical to the sequence of a certain virus. Once copied, these short RNAs form complexes with Cas proteins and serve as “guides” for them towards potential complementary viral DNAs. The viral DNA is destroyed by the Cas protein if it can hybridize with the short RNA from the CRISPR locus. Image created with BioRender.com. 

Interestingly, researchers have found an intriguing interconnection between transposons and the  CRISPR–Cas defense system. In a striking example a bacterial transposon has hijacked some of the genes of a CRISPR-Cas system and uses those genes for its own propagation in genomes. These transposons are called CRISPR-associated transposons. Such widespread exchange of genes is caused by the never-ending arms race between the transposons and their hosts’ defense systems. In the recent publication of Hoffman and colleagues, five Columbia postdocs Minjoo Kim, Leslie Y. Beh, Jing Wang, Diego R. Gelsinger, and Jerrin Thomas George collaborated and brought significant insight in the functioning of one CRISPR-associated transposon.

In their publication, the authors monitored in detail the formation of  this RNA-guided transposon and its associated complexes, which enabled them to resolve distinct protein recruitment events that take place before the integration of the transposon. They also found that even if initially hundreds of non-desired genomic sites are targeted for integration at the end only few of those sites recruit the whole transposon machinery that is required for integration at this genomic location. This discovery offered insights into how the potential target sites in the host genome are identified, screened and approved for integration, allowing the transposon system to be specific.

To advance the understanding of interactions responsible for the assembly of the transposon associated proteins, the authors determined the structure of one of the interacting proteins, named TnsC and found that it is forming rings of seven molecules of TnsC that can pass DNA through the central pore of the ring. This helps to correctly position DNA for the following integration (Figure 3). The resolved molecular structure also allowed to gain clarity on how TnsC mediates the communication between the proteins in this transposon complex that are responsible on the one hand for the targeting and on the other hand for the integration at a specific genomic location. Their results pointed to TnsC as the proofreading checkpoint that ensures the specific selection of genomic sites for transposition.

Figure 3. Upper part: Model of the 7 molecules of TnsC forming a ring through which DNA is passed. Lower part: Representative experimental result from Hoffmann et al. showing different configurations and views of the DNA-TnsC complex. Figure adapted from the original publication. 

In summary, the paper not only deciphers the molecular specificity of consecutive factor binding to genomic target sites in this interesting process of RNA-guided transposition, but the resolved detailed structure also provides valuable information for the development of future biotechnologies in the field of programmable and specific integration of DNA in desired genomic locations. Such technology differs from the above-described original CRISPR-Cas system currently used for genome editing, because it has the potential to be less mutagenic, as well as because it provides the opportunity to insert much longer pieces of DNA in a desired location. RNA-guided transposases hold tremendous potential for future biotechnological and human therapeutic applications and will without a doubt accelerate novel discoveries. Find out more in the original publication.

Reviewed by: Trang Nguyen & Maaike Schilperoort

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A high-tech device to effectively deliver drugs to tumors in the brain

Brain and other nervous system cancer is the 10th leading cause of death for men and women. Around 18,280 adults died from primary brain and central nervous system tumors in the United States in 2020. Glioblastoma is the most common malignant brain and other CNS tumors and median survival is only 12-15 months.

Why is brain tumor hard to treat? It is due to the blood brain barrier (BBB), a specialized network of blood vessels and cells that shields and protects the central nervous system against circulating toxins or pathogens that could cause brain infections. However, the impenetrability of the BBB also makes it difficult to treat tumors in the brain compared  to those in other organs. Patients with brain tumors have to receive higher doses of chemotherapy to penetrate the BBB and ensure an adequate amount of medication reaches into the brain and kills the tumor cells. The higher dose of chemotherapy will lead to the toxicity to the normal cells, which can result in serious side effects and even death of the patient. To overcome the BBB, scientists have tried to develop many different methods to deliver drugs effectively to the brain so that lower doses of chemotherapy can be used.

Over the last decade, Drs. Bruce and Canoll’s laboratory at the Columbia University Medical Center has been developing a new method to directly administer drugs to the site of the brain tumor, which they call convection enhanced delivery (CED). In CED, a small pump is implanted into the abdomen and connected to a thin catheter under the skin. Wireless technology is used to turn the pump on and off and control the flow rate of medicine that seeps in the tumor tissue.

In a recent study with the CED device, Dr. Bruce used topotecan, a drug that is toxic to glioblastoma cells, to treat five patients who were at least 18 years old with recurrent brain tumors. The patients were infused with topotecan for 48 hours, followed by a 5–7 day washout period before the next infusion, with four total infusions. Patients went about their normal routines at home while treatment continued without any severe side effects. After the fourth infusion, the pump was removed and the tumor was resected. This method is in the early phase of clinical trials (phase 1b) and will be expanded to a larger scale of patients due to test the safety and efficacy of the therapy for recurrent glioblastoma. This novel chemotherapy delivery strategy overcomes the limitation of drug delivery in patients with glioma. The results from this study have recently been published in Lancet Oncology 

There are two limitations in this study. First, there is no comparison group for determination of definitive survival benefit. Second, there is no way to assess the disease progression and treatment response due to effects of local drug infusion and surgical resection. However, in the locally delivered therapy (CED method), the authors used patients as their own control by performing pre-therapy and post-therapy MRIs and PET scans. The CED device effectively gets through the BBB to kill the brain tumor so new classes of drugs and targeted compounds could potentially be used such as high-molecular-weight compounds or viruses.

Reviewed by: Pei-Yin Shih, Maaike Schilperoort

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How our gut communicates with our brain to drive a preference for fat

Thanksgiving is just around the corner. The buttery sweet potato casserole, mashed potatoes, and gravy on the Thanksgiving dinner table are delicious and irresistible for most of us. Though fat from buttery food provides important building blocks for our body, overconsumption of fatty food could lead to weight gain and obesity-related diseases such as cardiovascular disease. To help keep our health in check, we need a better understanding of how fat consumption changes our desire for fatty food. A recent study led by Dr. Mengtong Li in the laboratory of Dr. Charles Zuker at the Zuckerman Mind Brain and Behavior Institute at Columbia University has started to reveal some insights. 

Previously the research team discovered how sugar preference was established. They found that among the two ways of processing the intake of sugar, taste and gut pathways, the preference for sugar arises from gut and is independent of taste. In line with this finding, the authors discovered that artificial sweeteners do not create a preference because they activate only taste receptors but not the gut pathway.

Built upon what they have learned from sugar preference, the authors first tested if mice have taste-independent preference for fat as well. They gave the mice a choice between oily water and water with artificial sweetener, and they recorded the number of times that the mice licked either of the water bottles as a measurement for preference. They found that the mice predominantly drank from the bottle with oily water two days after exposure to the two choices. Even when the authors directly delivered fat to the gut through surgery, or in mice that did not have taste receptors, the mice could still develop preference for fat. These observations suggested that mice could develop preference for fat through the gut pathway.

Figure 1. The gut-brain axis transfers information of fat intake from the gut to the brain. The orange arrow represents the direction of the information flow. The orange and red dots indicate the activation of the vagus nerve and cNST, respectively. The blue dots represent the hormone cholecystokinin (CCK). The figure was generated using BioRender.

How does the information of fat get transferred to the brain, along the so-called gut-brain axis, and make the mice want fat more than sugar? The authors traced the signals of fat stimuli from gut to brain (Figure 1) through pharmacological and genetic tools. They identified two receptors, G protein-coupled receptors GPR40 and GPR120, that function as fat detectors in the gut. Upon detecting the presence of fat, the gut then releases signaling molecules, including a satiety hormone cholecystokinin, to relay the information to the vagus nerve. Interestingly, while control mice do not have a preference for cherry- versus grape-flavored solutions, the authors were able to create a new preference in experimental mice by artificially activating the subset of vagal neurons that receive cholecystokinin signals from the gut. The vagus nerve travels from gut to brain, and eventually sends the fat signals to the brain region called the caudal nucleus of the solitary tract (cNST) in the brainstem.

Together, the identification of the gut-brain communication might help battle against overindulging in fatty foods. As stress eating could increase the consumption of high calorie foods, it would also be interesting to study how the gut-brain communication is modulated by different emotional states. 

Edited by: Maaike Schilperoort, Trang Nguyen, Sam Rossano

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Cleaning Up Data to Spruce Up the Results

Drawing conclusions from scientific studies can be difficult, in part because the data collected may be biased, which leads to a misinterpretation of the data. Let’s say we’re collecting data to investigate how many hours of sleep people get per night, during the week compared to over the weekend. We can ask 100 people their average nightly sleep time on weeknights and on weekends. To avoid bias, or skewing the data toward a particular duration, we should control for a few different factors. For example, we can limit our sample to only ask people 18 years or older, to avoid surveying children who tend to require more sleep than adults. This will avoid introducing a bias in the hours slept per night measure and prevent a trend in the data towards >8 hours a night. 

 

Some biases cannot be totally avoided during data collection. The existence of this unavoidable bias motivates scientists to consider including confounding variables in their data collection. Scientists use covariates when additional variables that change or differ across groups cannot be controlled for. A covariate is a variable that changes with the variable of interest, but isn’t of particular interest or importance for the question at hand. In our example, there are some other variables that may affect the amount of sleep an adult gets. This can include age (a postdoc in their late 20’s with a grant deadline might not get as much sleep as much as a retiree in their 60’s), activity level (strenuous physical activity leads to more sleep for better recovery), and caffeine intake (maybe serial coffee drinkers sacrifice an extra hour of sleep for an extra large cup in the morning). Because these variables may be different for each participant, we can measure them as observed covariates and include them in our statistical analysis.

 

Sometimes, as in the case with many epidemiological or public health studies, it’s difficult to measure or control for these covariates because the studies commonly use observational data from population-based studies which might not measure all potential covariates. In these studies, there may be unmeasured biases in the data that produce confounds, leading to imperfect conclusions in population studies. In our example, maybe we neglect to measure time spent on social media, which can affect someone’s total sleep time (I can’t be the only one who scrolls instagram instead of going to sleep at night…). Time spent on social media would be our unobserved covariate, which contributes to unmeasured bias in our sample. 

 

One way to address the problem of unmeasured bias is to pre-process the data – to fine-tune or clean up the data after it has been collected, but before statistical analysis is performed. In a recent paper, Columbia postdoc Dr. Ilan Cerna-Turoff and colleagues explored the use of a pre-processing method that can be used prior to data analysis in order to reduce the bias introduced by unmeasured covariates in a dataset. 

 

The pre-processing method investigated in this study is called “Full matching incorporating an instrumental variable (IV)” or “Full-IV Matching”, which aims to reduce biases between groups and thereby improve the accuracy of study findings. An instrumental variable (IV) is a measured variable that is unrelated to the covariates but is related to the variable of interest. For our example, an IV could how comfortable participants find their bed – something that is related to the time spent asleep, but isn’t related to the age or amount of coffee consumed. 

 

To apply the Full-IV Matching method, the researchers define an IV and “carve out” moderate values of the variable to focus on the extreme values (highest and lowest) across the range of IV measures, essentially ignoring the center of the data set. With this abridged dataset, the researchers implement a “matching” algorithm that pairs individuals who have similar values in their covariates, but who do not have similar values in their IV. In our example, participants who have similar caffeine intake levels or similar ages would be paired with participants who have the opposite bed-comfort level. This explores how the biases in the dataset change when each measured covariate is individually controlled for. Additionally, the researchers can define how much weight should be given to the unobserved covariate, depending on how much bias may be introduced into the data by this unobserved covariate. 

 

As proof-of-concept, Dr. Cerna-Turoff and colleagues simulated data from a scenario based on the Haitian Violence against Children and Youth Survey. Specifically, data were simulated based on measurements of social characteristics and experiences of young girls in Haiti, who were displaced either to a camp (“exposure” group) or to a wider community (“comparison” group) after the 2010 earthquake. The goal of this simulation experiment was to better understand how the displacement setting may be associated with risk of sexual violence. The researchers simulated data for 5 baseline covariates based on results from the Haitian Violence against Children and Youth Survey: (1) status of restavek (indentureship of poor children for rich families), (2) prior sexual violence, (3) living with parents, (4) age, and (5) social capital, of which the latter is an unobserved covariate. They also generated data for an exposure (camp or community), an outcome (sexual violence against girls), and an IV (earthquake damage severity). The researchers explored how the outcome was affected by the covariates and IV by quantifying the standardized mean difference of the variable across the exposure and comparison groups. A standardized mean difference value close to 0 indicates that the value of the variable was not different across the two groups, suggesting that this variable is not introducing bias into the analysis of group differences. 

 

The results suggest those who were displaced to a camp were at a higher risk of sexual violence than those who were displaced to a wider community, when correcting for all observed covariates. Additionally, the method successfully balanced the groups when correcting for the unobserved covariate of social capital. If not corrected for, differences in social capital might have confounded these results, such that girls with a stronger support network may appear to be at a lower risk. However, using the Full IV Matching method, bias across exposure and comparison groups for the observed covariates and the unobserved covariate of social capital was reduced, suggesting that neither the social capital nor the observed covariates contributed to the difference in risk for sexual violence observed between the two groups. 

 

This study provides a proof-of-concept for a pre-processing method for reducing bias across a data set. The authors mention limitations including the effect of the method on sample size and the ‘bias-variance trade-off’, in which increases in accuracy (less bias) may lead to more noise (higher variability) in the data. Ultimately, this type of methodology can aid in the correction of both observed and unobserved biases in population-based data collection, which has significant implications in epidemiologic studies, where not all sources of bias can be measured effectively.

 

Edited by: Emily Hokett, Pei-Yin Shih, Maaike Schilperoort; Trang Nguyen

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A how-to guide for improving the potency of stem cells

You may remember Dolly, the sheep that became famous in the ‘90s as the first mammal to be cloned from an adult cell. Dolly was created through somatic cell nuclear transfer (SCNT), in which the nucleus from a somatic donor cell, i.e., a cell from the body other than a sperm or egg cell, is transferred into an enucleated egg cell. In this case, the donor cell was derived from a sheep’s mammary glands, a medical term for the breasts. The scientists named the cloned sheep Dolly since they could not think of a more impressive pair of mammary glands than Dolly Parton’s, or so the story goes. Aside from generating viable embryos in the laboratory, SCNT can be used to generate human stem cell lines for research and therapeutic purposes. However, this procedure is technically challenging and requires egg cells, which raises ethical concerns.

Artist’s impression of Dolly Parton, the famous American country singer, holding the cloned sheep named after her.
© 2022, Maaike Schilperoort

In 2007, a lab in Kyoto, Japan, found another way of generating human stem cells. The group infected human skin cells with a virus that carried a set of genes known to be important for embryonic stem cells. This resulted in so-called “induced pluripotent stem cells”, or iPSCs, that are functionally identical to embryonic stem cells. Although therapeutically promising, these iPSCs do not have the same potency as the cells generated through SCNT. SCNT generates cells that are totipotent at an early stage, meaning that they can form viable embryos as well as extraembryonic tissues such as the placenta and yolk sack. In contrast, iPSCs are pluripotent and are not able to give rise to extraembryonic tissues. They also have an inferior differentiation potential and lower proliferation rate as compared to totipotent cells.

Efforts have been made by scientists to make embryonic stem cells and iPSCs more totipotent by treating them with small molecule inhibitors, resulting in so-called expanded potential stem cells (EPSCs) that that can give rise to the embryo as well as placenta tissues and thus are more versatile as compared to their pluripotent counterparts. However, the developmental potential of EPSCs is still inferior to true totipotent cells or cells generated through SCNT. To gain insight into how the developmental potential of EPSCs can be improved, Columbia postdoc Vikas Malik and colleagues performed a deep analysis of pluripotent embryonic stem cells vs. the more totipotent EPSCs. They examined gene expression, DNA accessibility, and protein expression, and found some unique genes and proteins that are upregulated in EPSCs as compared to embryonic stem cells, such as Zscan4c, Rara, Zfp281, and UTF1. This pioneering work, published in Life Science Alliance, shows us which genes and proteins to target to generate authentic totipotent stem cells in a petri dish.

The work of Dr. Malik and colleagues has improved our understanding of how to generate totipotent cells outside of the human body without having to deal with the technical and ethical challenges of SCNT. These cells can further improve stem cell therapy through a greater ability to regenerate and repair tissues affected by damage or disease. In addition, totipotent cells are more suitable to study early development and problems of the reproductive system, and are optimal for gene therapy to correct genetic defects that cause disease. As the word indicates, totipotent cells really hold all the power, and could greatly advance scientific knowledge and regenerative medicine.

More information on the pursuit of totipotency can be found in this comprehensive review article by Dr. Malik and his PI Jianlong Wang published in Trends in Genetics.

Reviewed by: Trang Nguyen and Vikas Malik

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